160 results about "Linear programming algorithm" patented technology

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention provides a multi-power-supply cooperative frequency modulation control method, and relates to the field of power grid dispatching, and the method comprises the steps: obtaining the real-time operation data of a power grid, the real-time operation data of the power grid comprises system frequency deviation, load prediction information, and the real-time output and adjustment capability data of multiple power supplies such as wind power, photovoltaic, hydroelectric and thermal power; according to the real-time operation data of the power grid, through a mixed integer linear programming algorithm, taking power generation cost minimization as a target function, and taking multi-power output upper and lower limits, a climbing rate, a system frequency deviation limit value and the like as constraint conditions, calculating an optimal output distribution scheme of multiple power supplies; and converting the optimal output distribution scheme into a power supply output adjustment instruction. Cooperative frequency modulation of multiple power supplies is realized, the system frequency stability and economical efficiency are improved, the renewable energy source grid connection and consumption capability is enhanced, and an effective solution is provided for large-scale new energy source access to a power grid.

The invention relates to the technical field of steel production plan optimization based on multi-dimensional constraint dynamic coupling, in particular to a molten iron operation plan arrangement method based on dynamic constraint modeling hybrid optimization, which comprises the following steps: acquiring order data, equipment state data and constraint rule data to generate structured input parameters comprising order priority labels; constructing a multi-objective optimization model based on the molten iron distribution constraint and the maintainable time length of the equipment, extracting the maintenance conflict relationship between the equipment groups, constructing a maintenance rule knowledge graph, and generating a dynamic constraint condition set; generating an initial molten iron distribution and maintenance plan through global search by adopting a genetic algorithm, and outputting a molten iron distribution path and an equipment maintenance time sequence through local optimization by combining a linear programming algorithm; according to the method, dynamic collaborative optimization of molten iron distribution and equipment maintenance is achieved, and the resource utilization rate, the order delivery rate and the production system stability are improved.

The invention discloses a dynamic production plan adjustment method in an industrial internet environment, and the method comprises the steps: constructing a task dependence graph according to the task attribute data of a production task; identifying a task state change event, and updating the task dependency graph in real time based on task nodes and edges influenced by the task state change event; a linear programming algorithm is adopted to obtain a task priority ranking result, and task priority ranking is dynamically adjusted; a distributed scheduling framework is adopted to map tasks to scheduling queues of all edge nodes, and a task distribution strategy is determined; and marking the abnormal task based on the task performance index in the task execution process, identifying the abnormal mode of the abnormal task, and optimizing the task allocation strategy based on the abnormal mode data of the abnormal task. The invention provides a dynamic, self-adaptive and efficient production plan adjustment method, which can comprehensively improve the global optimization capability, real-time performance and intelligent level of task scheduling in a dynamic production environment.

The invention relates to a load regulation and control method and system based on reinforcement learning, and the method comprises the steps: 1, decomposing a multi-time scale optimization problem into a plurality of layers in a source network load storage management and control scene through employing a layered optimization frame, enabling each layer to correspond to a different time scale, and carrying out the optimization; a model prediction control algorithm is adopted in the short-term optimization layer, a mixed integer linear programming algorithm is adopted in the medium-term optimization layer, and a reinforcement learning method is adopted in the long-term optimization layer to solve a multi-objective optimization problem; and 2, long-term load regulation and control decision making: controlling the power grid to make different load regulation and control decisions through an actually adjustable reward mechanism based on reinforcement learning, so that the intelligent power grid load regulation and control system adapts to continuously changing environments and targets, and the flexibility and adaptability of the system are improved. According to the invention, the reliability and stability of power supply can be ensured, fine management and regulation of power loads are realized, and the operation efficiency, reliability and safety of a power system are improved.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention discloses a heat supply whole network collaborative dynamic optimization regulation and control method, system and equipment and a storage medium, relates to the technical field of heat supply control, and aims at solving the technical problems that in the prior art, thermal load prediction data is inaccurate, and heat transfer distribution cannot be optimized by a heat supply system, so that operation energy consumption is large. According to the technical scheme, the method mainly comprises the steps that building load prediction data and heating station load prediction data are acquired, and a load prediction model is constructed; training the load prediction model, and optimizing the model and pipe network data by using an ARX active regression algorithm; a heat supply whole network physical simulation model is constructed, and heat supply whole network thermal load data is obtained through calculation by using an MILP mixed integer linear programming algorithm; an optimal control strategy is obtained by optimizing a heat supply whole network physical simulation model and introducing a real-time feedback mechanism and an overall cooperative control module, optimization parameters of the heat supply whole network physical simulation model are automatically adjusted by using the optimal control strategy, and the heat supply whole network is continuously in an optimal operation state through automatic repeated iteration.

The invention discloses an intelligent medical equipment management system and method, and the method comprises the steps: achieving the decentralized credible storage of an equipment use record through a block chain technology, and guaranteeing the safety and traceability of data through an SHA-256 salted Hash and Merkle tree structure; an intelligent contract is integrated to realize automatic execution of an inventory strategy, and an intelligent scheduling system is constructed in combination with an ARIMA-LSTM hybrid model and a linear programming algorithm. The system covers core modules of data acquisition, block chain storage, smart contract, prediction optimization and the like, solves the problems of data potential safety hazards, low inventory efficiency and scheduling blindness in traditional management, is suitable for intelligent management of the medical equipment in the whole life cycle, and remarkably improves the medical resource utilization efficiency and management compliance.

The invention relates to the technical field of network security, in particular to a satellite internet threat identification method and system and electronic equipment, and the method comprises the steps: constructing a node reputation evaluation model through a block chain technology, obtaining trusted nodes, constructing a satellite network, and collecting inter-satellite link information in real time; acquiring a satellite network topology change in real time, constructing an inter-satellite link security evaluation model, and generating a self-adaptive security policy matched with the inter-satellite link security level; dynamically calculating and optimizing a data transmission path through a mixed integer linear programming algorithm; constructing an inter-satellite link rapid switching model to analyze inter-satellite link information, generating inter-satellite link abnormal information, and rapidly switching to a standby inter-satellite link according to a self-adaptive security policy and network topology information; performing data transmission according to the inter-satellite link, and monitoring and acquiring transmission behavior data; a data exception recognition model is constructed to analyze the transmission behavior, and a data exception response is obtained; and identifying the satellite network threat according to the data exception response.

The invention belongs to the field of distributed multi-energy complementary system planning and operation optimization, and particularly discloses a distributed multi-energy complementary system design and operation hybrid optimization method considering electric vehicle energy storage. According to the building load clustering method and device, the DBSCAN density clustering algorithm, the fuzzy C-means algorithm, the fast elitist multi-target genetic algorithm (NSGA-II), the mixed integer linear programming algorithm (MILP) and the entropy weight method are combined, building load clustering is achieved based on the DBSCAN density clustering algorithm and the fuzzy C-means algorithm, and the complexity of the optimization calculation process is reduced. By combining NSGA-II and MILP, integrated optimization of capacity planning and an operation scheme of the distributed multi-energy complementary system considering energy storage of the electric vehicle is realized; and based on an entropy weight method, screening an optimal solution in a Pareto frontier solution set of capacity planning, and obtaining an optimal operation scheme of the system under the planning. The method provided by the invention fills the blank of the planning and operation optimization technology of the distributed multi-energy complementary system considering the energy storage of the electric vehicle.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention discloses a power distribution network reconstruction method and device based on the Stackelberg game, and belongs to the technical field of electric power systems, and the reconstruction method comprises the steps: modeling the attack and defense confrontation of an attacker and a defender as the Stackelberg game, taking the attacker as a leader, the defender as a follower, and taking network reconstruction as a defense means; a blog framework is provided for formulating an optimal defense means; and fusing the game framework and a mixed integer linear programming algorithm, and calculating an optimal defense means of the defender by solving a Stackelberg equilibrium point. According to the method, the optimal defense scheme for coping with a strategic attacker can be calculated, and the uncertainty of attack positioning is considered in decision making, so that the minimum system reconstruction cost is realized while the voltage safety of the power grid is ensured, and the safety and the toughness of the power distribution network under network attacks are effectively improved.

The invention relates to the technical field of medical informatization management, and provides a hospital operating room scheduling optimization method and system based on multi-dimensional constraint planning, and the method comprises the steps: responding to an operation scheduling request; obtaining operation scheduling basic data to be scheduled and optimized, and constructing a data association input table including mapping relationships among departments, diagnosis and treatment groups, operating rooms and operation applications; generating an allocation variable set for scheduling variable control based on the data association input table, constructing a condition constraint set to constrain a scheduling variable, and forming a scheduling decision objective function by constructing at least two objective optimization functions through weighted combination; and calling a branch and bound algorithm to solve a scheduling variable solution to generate an optimized operation scheduling plan. By integrating hospital operation related data and applying a constraint planning technology to process multi-dimensional constraints such as time and resources, automation and intellectualization of operation scheduling are achieved, and by constructing a mathematical optimization model and applying a linear programming algorithm, the efficiency and rationality of operation scheduling are improved.

A regional building cooling and heating load artificial neural network energy distribution optimization method disclosed by the present invention comprises the steps of obtaining inter-regional topological relation data and load-related data, constructing a graph network model to capture spatial dependency, fusing meteorological data and historical load records to generate a multivariable dynamic data set, and obtaining the optimal energy distribution of the cooling and heating load artificial neural network. Processing the multivariable dynamic data set by adopting a graph convolutional neural network to obtain an initial prediction result, adjusting the initial prediction result through an artificial neural network to generate adjusted load distribution data, and analyzing by combining a heat transfer rule and a time sequence to obtain spatial and temporal distribution characteristics; and integrating equipment efficiency constraint and cost fluctuation data by adopting a multi-objective optimization algorithm to obtain an optimized objective function value, adjusting equipment dynamic parameters through deep reinforcement learning to obtain a parameter adjustment scheme, and optimizing an energy distribution proportion by adopting a linear programming algorithm to obtain a final energy distribution scheme.

The invention discloses a multi-objective hierarchical rolling optimization scheduling method and system for off-grid wind-solar green hydrogen production. The method comprises the following steps: firstly, establishing a total power balance model of an off-grid wind-solar hydrogen production scene, secondly, respectively establishing charging and discharging models of an energy storage device, introducing integer state variables of 0 and 1 to uniformly describe the independent charging and discharging models, and then respectively setting physical constraints. And finally, by establishing a multi-target hierarchical optimization proposition and according to the model and constraint established in the previous step, real-time wind and light power generation power and future power prediction data, performing optimization solution by adopting a nonlinear programming algorithm mixed with 0-1 integers. The system researched and developed based on the method can update a scheduling plan in a rolling manner through a prediction model and optimization according to high and low priority optimization targets set by an operator based on real-time and predicted wind-solar power generation power data, so that the dynamic optimization of a scheduling strategy is ensured, and reliable operation guidance is provided for wind-solar hydrogen production scheduling personnel in real time.

The invention discloses a building cluster demand response coordinated multi-objective optimization scheduling method and system, and the method comprises the steps: predicting the energy demand and supply information of a building cluster, and building an energy transaction model of a building unit and the cluster based on renewable energy supply and battery storage electric energy; the building cluster is integrated into a single entity, the energy supply of the single entity is the total energy supply of all buildings, the energy demand is the total energy demand of all buildings, the battery capacity is the total battery capacity of all buildings, and the energy transaction model takes a cost function of the whole cluster as a target function; and optimizing the overall cost function of the building cluster through an iterative demand response algorithm, calculating to obtain an optimal operation parameter, controlling the operation of each building unit according to the optimal operation parameter of the cluster by using a nonlinear programming algorithm, and carrying out multi-target optimization scheduling on the building cluster. Flexible resources in the building cluster can be fully utilized, and efficient utilization and supply and demand balance of energy are achieved.

The invention discloses an electric power material warehouse intelligent scheduling strategy based on an improved A-star and whale optimization algorithm, and belongs to the technical field of artificial intelligence, and the strategy comprises the steps: S1, obtaining various data information contained in an electric power material warehouse; establishing a database; s2, updating the material information in the database according to the map information; s3, obtaining the name of a required cargo, searching a database to query the position information of the cargo, and providing position selection for the cargo with a plurality of positions by adopting a mixed integer linear programming algorithm; s4, performing path planning by using an improved A-star algorithm according to the starting point of the unmanned vehicle and the cargo position information, and constructing an initial path of each section of pickup of the unmanned vehicle; s5, optimizing the path; according to the electric power material warehouse intelligent scheduling strategy based on the improved A-star and whale optimization algorithm, the occupied system memory during path planning can be effectively reduced, the situation that a local optimal solution is trapped in a complex environment is avoided, and a new scheme and thought are provided for the path planning method.

The invention provides an operation control method and system for a modular energy storage shelter, and the method comprises the steps: obtaining comprehensive operation data through collecting the operation parameters of all energy storage modules, building an energy storage module health state evaluation model through a machine learning algorithm, and carrying out the operation control of the modular energy storage shelter. Meanwhile, real-time power demand data is obtained from an API interface of a power grid company, a user-side intelligent electric meter, historical data and current environment parameter analysis, a charging and discharging strategy of each energy storage module is dynamically adjusted by adopting a genetic algorithm to obtain a charging and discharging plan, and a power supply and demand change trend is predicted by combining a deep learning technology to generate a prediction result. An optimal energy storage module scheduling scheme is confirmed by adopting a mixed integer linear programming algorithm to obtain a scheduling instruction, a decision process is continuously optimized through online learning, an optimal control instruction is generated by utilizing a self-adaptive control strategy of reinforcement learning, and according to the technical scheme provided by the invention, energy waste is effectively reduced, the service life is prolonged, and maintenance and replacement are reduced; therefore, operation cost is reduced and economic benefits are improved.

The invention discloses an unmanned aerial vehicle hunting resource allocation method based on confrontation generation imitation learning in a pursuit scene, and aims to determine a pursuit target of each pursuit unmanned aerial vehicle in a many-to-many pursuit scene so as to solve the problem of hunting resource allocation. The method comprises the following steps: firstly, establishing a system model for a many-to-many-chasing unmanned aerial vehicle escape scene and carrying out symbol declaration; and then introducing the concept of an Apollonius circle, and establishing a potential game model for unmanned aerial vehicle hunting resource allocation. And then, providing an optimal surrounding resource allocation scheme solving algorithm based on linear programming. And finally, providing an unmanned aerial vehicle hunting resource allocation algorithm based on confrontation generation imitation learning. The algorithm designed by the invention is not influenced by the number and the initial state of the unmanned aerial vehicles of both pursuit and escape parties, Nash equilibrium under a potential game can be searched by adopting a linear programming algorithm, and an optimal hunting resource allocation scheme is generated based on adversarial generation imitation learning and adaptive learning; and finally, real-time dynamic allocation and scheduling of surrounding resources in a many-to-many unmanned aerial vehicle pursuit scene are realized.

The invention relates to the technical field of electric power material distribution management, particularly discloses an electric power material distribution management system and method based on big data, and aims to solve the problems of dispersed material data, inaccurate demand prediction, low distribution efficiency, improper inventory management and the like in an existing system. According to the specific technical scheme, the method comprises the steps of integrating material names, material description information and supplier information, and establishing a unified electric power material database; carrying out preliminary classification and refined classification on the materials by using a clustering algorithm, and adding a label for each type of materials; historical demand data are analyzed, a demand prediction model is constructed, and the prediction precision is improved; dynamic optimal distribution path planning is realized by utilizing a geographic information system technology and a linear programming algorithm, and the distribution efficiency is improved; the stock is dynamically adjusted by calculating the safe stock and the reorder cycle, and stock overstock or shortage is avoided; the distribution plan is dynamically adjusted according to the inventory level and the demand frequency, the distribution process is monitored in real time, and on-time distribution is ensured.

The invention discloses an improvement method of a large language model, electronic equipment and a storage medium, and the method comprises the steps: calculating a correlation matrix between middle layers through a public data set, and calculating mutual information redundancy between the layers based on the correlation matrix between the middle layers, an inter-layer redundancy matrix is constructed through mutual information redundancy among the layers; calculating the total redundancy of each layer based on the redundancy matrix, and generating a layer importance factor through function conversion; establishing an objective function based on the layer importance factors and constructing a linear optimization model of sparse rate distribution of each layer; solving the optimization model by adopting a linear programming algorithm to obtain an optimal sparse rate distribution scheme of each layer; differentiated pruning is carried out on each layer of weight matrix according to the distribution scheme, a weight parameter with the maximum amplitude in each layer of weight matrix is reserved, the rest parameters are set to be zero, and a sparse weight matrix is obtained; according to the method, the features can be selectively pruned in the middle layer, so that fine-grained optimization of a large language model is realized.

The invention discloses a mixed integer linear programming-based iron and steel industry power grid interactive full-process scheduling method. The method comprises the specific steps of S1, constructing a dynamic balance equation and an energy storage equipment dynamic management model; s2, establishing a multi-energy coupling collaborative optimization model; s3, setting a target function, an energy storage equipment constraint condition and a key equipment power constraint condition; s4, embedding a demand response load adjustment model; and S5, based on the model embedded in the step S4, solving by using a mixed integer linear programming algorithm to obtain an optimal scheduling scheme. According to the iron and steel industry power grid interactive full-process scheduling method based on mixed integer linear programming, by constructing an iron making-steel making-rolling full-process energy flow dynamic coupling model, secondary energy balance of coal gas, steam and the like and power grid demand response instructions are integrated, and collaborative optimization of energy and production between processes is achieved.

The invention discloses a safety control method of a hydrogen power source. The method comprises the following steps: parameter initialization: setting and initializing electrolytic cell efficiency curve data, control parameters, power generation parameters and a reference operation state of a hydrogen production electrolytic cell; and data acquisition: power prediction data, running state information (including temperature, pressure and current) of the electrolytic cell and real-time power generation data of power generation equipment (photovoltaic / wind power) are acquired in real time. According to the safety control method for the hydrogen power source, efficiency curve data and control parameters of the electrolytic cell are accurately set through the parameter initialization step, and a foundation is laid for efficient operation. And a real-time data acquisition and power generation equipment state judgment step: dynamically adjusting a control strategy according to the grid-connected state of the power generation equipment, maximizing the power generation capacity or realizing smooth power output, and reducing energy waste. In the control instruction generation step, optimal set power is calculated by using a mixed integer linear programming algorithm, the electrolytic cell is ensured to operate in a high-efficiency area, and the system efficiency is integrally improved.

The invention discloses a synergistic proportioning method for multiple scrap aluminum capable of reducing the proportion of pure aluminum ingots, and relates to the technical field of aluminum smelting and secondary aluminum, the method comprises the following steps: S1, collecting and sorting scrap aluminum: collecting any three scrap aluminum from door and window scrap aluminum, automobile scrap aluminum, zip-top can scrap aluminum and industrial leftover scrap aluminum, collecting, and removing impurities in the scrap aluminum; s2, component detection: respectively detecting the aluminum content and impurity elements of each scrap aluminum in the step S1 by using a direct-reading spectrometer; according to the method, the proportion is automatically calculated through a linear programming algorithm, traditional empirical proportion is replaced, the proportion calculation time is shortened, and the batching efficiency is greatly improved. Meanwhile, the qualified rate of finished product detection is improved, the number of reworking times is reduced, the production cycle is shortened, the production efficiency and market response capability of enterprises are improved, and the enterprises are helped to quickly meet customer order requirements.

The invention discloses a production resource scheduling method for a container bottom plate, and relates to the technical field of container manufacturing, and the method comprises the steps: S1, collecting heat conductivity coefficient data and local temperature gradient distribution data in a bottom plate machining process through a sensor array, processing the data through a Fourier transform method, and extracting low-frequency domain features; the space-time non-uniform change trend of the heat conductivity coefficient is obtained; s2, according to the space-time non-uniform change trend, judging whether the current processing state deviates from a preset heat-conducting property threshold value or not, if the current processing state deviates from the preset heat-conducting property threshold value, starting a linear programming algorithm through constraint condition setting and objective function definition, optimizing a material adding proportion and temperature control parameters, and determining an adjusted parameter combination; according to the production resource scheduling method for the container bottom plate, the stability of the heat conduction performance is remarkably improved, resource consumption is reduced, and efficient and accurate control over micro-scale machining is achieved.

The invention discloses a multi-energy system optimization method and system based on hydrogen energy storage, and belongs to the field of multi-energy system optimization, and the method comprises the steps: obtaining the historical wind speed, wind direction and solar irradiance data of an anemometer tower and a photovoltaic power station, training a time sequence analysis model through the historical wind speed, wind direction and solar irradiance data, and obtaining a time sequence analysis model; wind energy power and solar energy power prediction curves in a future time period are obtained; the method comprises the following steps: acquiring pressure and temperature data of a hydrogen storage tank, inputting the pressure and temperature data into a pre-established leakage rate dynamic compensation algorithm to obtain a real-time hydrogen loss amount, and if the hydrogen loss amount is greater than a preset threshold value, reducing the operation pressure or temperature of the hydrogen storage tank; and according to the wind and light power prediction curve, the hydrogen production equipment operation data, the hydrogen storage tank operation parameters, the hydrogen conversion equipment control signal, the constraint model and the income cost model as input of a linear programming algorithm, an equipment capacity configuration scheme for maximizing the system economic benefits is obtained.

The invention provides a data center power monitoring method and system based on remote management, and the method comprises the steps: determining a power utilization predicted value of a power utilization device in a real-time target time period based on historical power utilization data and real-time power utilization data of a target power utilization device; a linear programming algorithm is adopted, the power consumption predicted value is combined, the total power supply amount is distributed, and a real-time power distribution value of the target power consumption equipment is determined; the performance index of the target electric equipment is obtained, the operation parameters of the target electric equipment are adjusted according to the real-time power distribution value and the performance index, and the operation parameters meet the operation requirements of the target electric equipment. According to the application, the power consumption data of the target power consumption equipment can be monitored, the power consumption predicted value in the future time is predicted in combination with the historical power consumption data, and the power is distributed to the total power supply in combination with the power consumption predicted value, so that the operation parameters of each target power consumption equipment meet the operation parameters, and the overall operation efficiency of the power consumption equipment is improved.

The invention provides an efficient energy-saving power distribution method and system for a shelter hospital. Wherein the real-time power utilization demand of each power utilization unit of the shelter hospital is collected by utilizing a sensor network, and an integrated energy demand report is generated by combining external meteorological conditions; based on the comprehensive energy demand report, automatically adjusting a power distribution proportion by using a mixed integer linear programming algorithm, and generating a key equipment priority list by using an analytic hierarchy process; based on the key equipment priority list, analyzing a topological structure by using a network flow optimization algorithm based on a graph theory, evaluating the comfort level of a user by using a fuzzy logic technology, and generating an optimized power dispatching plan; and based on the optimized power dispatching plan, regularly examining the power distribution effect, optimizing the power distribution model, and generating the efficient and energy-saving power distribution system. According to the technical scheme, the power distribution proportion can be adjusted in real time, and the energy utilization efficiency is improved.

The present invention relates to the technical field of production process optimization, specifically to a molybdenum wire production process optimization system based on cloud computing, the system includes an energy analysis module, an equipment scheduling module, a demand prediction module, a real-time control module, an operation priority module, and a cycle scheduling module. The energy analysis module is based on energy consumption data, uses a cloud computing platform to gather full-process data, analyzes the temperature and time changes during the operation of the molybdenum wire heating furnace, simulates the energy consumption under differentiated heating conditions, and forms an energy consumption distribution map. In the present invention, the demand prediction module uses a linear programming algorithm and combines historical energy data to accurately predict future energy demand, improve energy efficiency, and significantly reduce waste. The operation priority module uses an SJF algorithm to optimize the priority sorting of production tasks, improve the task scheduling process, make the operation efficiency and sequence arrangement more reasonable, and make the production process smoother to reduce production delays.